Wellbore drilling, such as is used for petroleum exploration and production, includes rotating a drill bit while applying axial force to the drill bit. The rotation and the axial force are typically provided by equipment which includes a drilling “rig”. As is known to those of ordinary skill in the art, the rig includes various devices to lift, rotate, and control segments of drill pipe which ultimately connect the drill bit to the equipment on the rig. The drill pipe includes a through bore through which drilling fluid is pumped. The drilling fluid discharges through orifices in the bit (“jets”) for the purposes of cooling the drill bit and lifting rock cuttings out of the wellbore as it is being drilled.
The speed and economy with which a wellbore is drilled, as well as the quality of the borehole, depend on a number of factors. These factors include, among others, the mechanical properties of the rocks which are drilled, the diameter and type of the drill bit used, the flow rate of the drilling fluid, and the rotary speed and axial force applied to the drill bit. In general, for any particular mechanical property of a formation, the rate of penetration (ROP) of a drill bit tends to be related to the axial force on and the rotary speed of the drill bit. The rate at which the drill bit wears out also tends to be related to the ROP. Various methods have been developed to select drilling parameters to achieve certain desirable results, for example, improved ROP and reduced drill bit wear.
Commonly assigned U.S. Pat. No. 6,424,919 (“the '919 patent”) discloses a method of selecting a drill bit design parameter by inputting at least one property of a formation to be drilled into a trained Artificial Neural Network (ANN). The '919 patent also discloses that a trained ANN may be used to determine optimum drilling operating parameters for a selected drill bit design in a formation having particular properties. The ANN may be trained using data obtained from laboratory experimentation or from existing wells that have been drilled near the present well, such as an offset well.
ANNs are known to emulate the neuron interconnection architecture of the human brain to mimic the process of human thought. By using empirical pattern recognition, ANNs have been applied in many areas to provide sophisticated data processing solutions to complex and dynamic problems (e.g., classification, diagnosis, decision making, prediction, voice recognition, and military target identification).
Similar to the human brain's problem solving process, ANNs use information gained from previous experience and apply that information to new problems and/or situations. The ANN uses a “training experience” (e.g., including a training data set) to build a system of neural interconnects and weighted links between an input layer (independent input variables), a hidden layer of neural interconnects, and an output layer (at least one dependent output variable or result). No existing model or known algorithmic relationship between these variables is required, but such relationships may be used to assist in training the ANN when available. An initial determination of the output variables in the training exercise is compared with the actual values in a training data set. Differences are back-propagated through the ANN to adjust the weighting of the various neural interconnects, until the differences are reduced to the user's error specification. Due largely to the flexibility of the learning algorithm, non-linear dependencies between the input and output layers can be “learned” from experience.
Commonly assigned, co-pending U.S. patent application Ser. No. 11/670,696 (U.S. Patent Publication 2007/0185696) discloses a method for determining optimized drilling parameters in substantially real-time during drilling. Data is collected from the well while drilling and employed in a drilling optimization system. The data may include, for example, lithologic and compression data obtained from cuttings, logging and measurement while drilling data, ROP data, drilling fluid composition, and the like. The optimization system has access to or includes various ANNs suitable for determining optimized drilling parameters based on historical and real-time data.
While the above described methods for determining drilling parameters have been utilized commercially, there is room for further improvement. For example, the above described prior art methods are configured for a bottom hole assembly (BHA) including only a single cutting structure (e.g., a conventional drill bit deployed at the lower end of the BHA). However, BHA configurations that employ two (or even three) distinct cutting structures (e.g., a drill bit and one or more hole openers or underreamers) are commonly employed. These cutting structures typically include distinct cutting surfaces, and being longitudinally spaced in the BHA, commonly simultaneously cut distinct formation lithologies having correspondingly distinct physical properties. Therefore there is a need in the art for improved drilling optimization methods, and particularly for drilling optimization methods that are suitable for use with a BHA configuration having multiple cutting structures.